{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2024机器智能 实验一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:19:28.489347Z",
     "iopub.status.busy": "2024-02-27T14:19:28.488894Z",
     "iopub.status.idle": "2024-02-27T14:19:28.495302Z",
     "shell.execute_reply": "2024-02-27T14:19:28.493892Z",
     "shell.execute_reply.started": "2024-02-27T14:19:28.489304Z"
    }
   },
   "outputs": [],
   "source": [
    "# File: 2024机器智能实验01_teacher.ipynb\n",
    "# License: MIT License \n",
    "# Copyright: (c) 2024 Rongxi Li <lirx67@mail2.sysu.edu.cn> \n",
    "# Created: 2024-02-27 \n",
    "# Brief: 2024机器智能实验课教师版——实验一"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 必要依赖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:19:28.497932Z",
     "iopub.status.busy": "2024-02-27T14:19:28.497127Z",
     "iopub.status.idle": "2024-02-27T14:20:03.827344Z",
     "shell.execute_reply": "2024-02-27T14:20:03.825756Z",
     "shell.execute_reply.started": "2024-02-27T14:19:28.497885Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.47.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy<2,>=1.20 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.24.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.829866Z",
     "iopub.status.busy": "2024-02-27T14:20:03.829474Z",
     "iopub.status.idle": "2024-02-27T14:20:03.836883Z",
     "shell.execute_reply": "2024-02-27T14:20:03.834987Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.829827Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "from typing import List, Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.841194Z",
     "iopub.status.busy": "2024-02-27T14:20:03.840762Z",
     "iopub.status.idle": "2024-02-27T14:20:03.854218Z",
     "shell.execute_reply": "2024-02-27T14:20:03.852357Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.841158Z"
    }
   },
   "outputs": [],
   "source": [
    "import base64\n",
    "from IPython.display import Image, display\n",
    "\n",
    "\n",
    "def mm(graph):\n",
    "    graphbytes = graph.encode(\"utf8\")\n",
    "    base64_bytes = base64.b64encode(graphbytes)\n",
    "    base64_string = base64_bytes.decode(\"ascii\")\n",
    "    display(Image(url=\"https://mermaid.ink/img/\" + base64_string + \"?bgColor=FFFFFF\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Python基础"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.1 helloworld"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.856490Z",
     "iopub.status.busy": "2024-02-27T14:20:03.855930Z",
     "iopub.status.idle": "2024-02-27T14:20:03.873524Z",
     "shell.execute_reply": "2024-02-27T14:20:03.872234Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.856439Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helloworld\n"
     ]
    }
   ],
   "source": [
    "print(\"helloworld\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.2 变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.876073Z",
     "iopub.status.busy": "2024-02-27T14:20:03.875571Z",
     "iopub.status.idle": "2024-02-27T14:20:03.890648Z",
     "shell.execute_reply": "2024-02-27T14:20:03.888931Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.876035Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 6.1\n",
      "HELLO hello\n",
      "True False\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 整型、浮点型\n",
    "num_int = 5\n",
    "num_float = 6.1\n",
    "print(num_int, num_float)\n",
    "\n",
    "# 字符串\n",
    "s1 = \"HELLO\"\n",
    "s2 = \"hello\"\n",
    "print(s1, s2)\n",
    "\n",
    "# 布尔\n",
    "t = True\n",
    "f = False\n",
    "print(t, f)\n",
    "\n",
    "# NoneType\n",
    "num = None\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.894486Z",
     "iopub.status.busy": "2024-02-27T14:20:03.893783Z",
     "iopub.status.idle": "2024-02-27T14:20:03.905490Z",
     "shell.execute_reply": "2024-02-27T14:20:03.904342Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.894439Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "char\n"
     ]
    }
   ],
   "source": [
    "# 变量可以被任意类型赋值\n",
    "v = 5\n",
    "print(v)\n",
    "v = \"char\"\n",
    "print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.3 运算符"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:bae32a38-d3b8-40f2-805a-8e2f70c333c8.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.907323Z",
     "iopub.status.busy": "2024-02-27T14:20:03.906923Z",
     "iopub.status.idle": "2024-02-27T14:20:03.922545Z",
     "shell.execute_reply": "2024-02-27T14:20:03.921270Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.907290Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1\n",
      "1.5\n"
     ]
    }
   ],
   "source": [
    "# 计算运算符\n",
    "# 整形加浮点型\n",
    "print(1 + 2.1)\n",
    "\n",
    "# 整型除法\n",
    "print(3 / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.924591Z",
     "iopub.status.busy": "2024-02-27T14:20:03.924191Z",
     "iopub.status.idle": "2024-02-27T14:20:03.936826Z",
     "shell.execute_reply": "2024-02-27T14:20:03.935784Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.924558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# 比较运算符\n",
    "# 判断是否等于\n",
    "print(1 + 1 == 2)\n",
    "\n",
    "# 判断是否大于等于\n",
    "print(1 + 1 >= 2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.938835Z",
     "iopub.status.busy": "2024-02-27T14:20:03.938372Z",
     "iopub.status.idle": "2024-02-27T14:20:03.955395Z",
     "shell.execute_reply": "2024-02-27T14:20:03.953537Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.938789Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# 逻辑运算符\n",
    "print(1 + 1 == 2 or 2 + 2 == 3)\n",
    "print(1 + 1 == 2 and 2 + 2 == 3)\n",
    "print(not (1 + 1 == 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.4 条件语句"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.957990Z",
     "iopub.status.busy": "2024-02-27T14:20:03.957510Z",
     "iopub.status.idle": "2024-02-27T14:20:03.971926Z",
     "shell.execute_reply": "2024-02-27T14:20:03.970748Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.957926Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number is less than 10\n"
     ]
    }
   ],
   "source": [
    "number = 5\n",
    "if number == 10:\n",
    "    print(\"Number is 10\")\n",
    "elif number < 10:\n",
    "    print(\"Number is less than 10\")\n",
    "else:\n",
    "    print(\"Number is more than 10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.974960Z",
     "iopub.status.busy": "2024-02-27T14:20:03.974235Z",
     "iopub.status.idle": "2024-02-27T14:20:03.984958Z",
     "shell.execute_reply": "2024-02-27T14:20:03.983687Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.974891Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes it is available\n"
     ]
    }
   ],
   "source": [
    "is_available = True\n",
    "if is_available:\n",
    "    print(\"Yes it is available\")\n",
    "else:\n",
    "    print(\"Not available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:03.994568Z",
     "iopub.status.busy": "2024-02-27T14:20:03.993046Z",
     "iopub.status.idle": "2024-02-27T14:20:04.008005Z",
     "shell.execute_reply": "2024-02-27T14:20:04.006155Z",
     "shell.execute_reply.started": "2024-02-27T14:20:03.994462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes it is available\n"
     ]
    }
   ],
   "source": [
    "is_available = 9\n",
    "if is_available:\n",
    "    print(\"Yes it is available\")\n",
    "else:\n",
    "    print(\"Not available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.012188Z",
     "iopub.status.busy": "2024-02-27T14:20:04.010692Z",
     "iopub.status.idle": "2024-02-27T14:20:04.022443Z",
     "shell.execute_reply": "2024-02-27T14:20:04.021095Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.012124Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not available\n"
     ]
    }
   ],
   "source": [
    "is_available = 0\n",
    "if is_available:\n",
    "    print(\"Yes it is available\")\n",
    "else:\n",
    "    print(\"Not available\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.025252Z",
     "iopub.status.busy": "2024-02-27T14:20:04.024481Z",
     "iopub.status.idle": "2024-02-27T14:20:04.044458Z",
     "shell.execute_reply": "2024-02-27T14:20:04.042799Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.025202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not available\n"
     ]
    }
   ],
   "source": [
    "is_available = None\n",
    "if is_available:\n",
    "    print(\"Yes it is available\")\n",
    "else:\n",
    "    print(\"Not available\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.5 循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.046926Z",
     "iopub.status.busy": "2024-02-27T14:20:04.046279Z",
     "iopub.status.idle": "2024-02-27T14:20:04.076980Z",
     "shell.execute_reply": "2024-02-27T14:20:04.075307Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.046889Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number 1\n",
      "Number 2\n",
      "Number 3\n",
      "Number 4\n"
     ]
    }
   ],
   "source": [
    "# for循环\n",
    "for i in range(1, 5):\n",
    "    print(\"Number\", i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.078973Z",
     "iopub.status.busy": "2024-02-27T14:20:04.078534Z",
     "iopub.status.idle": "2024-02-27T14:20:04.096367Z",
     "shell.execute_reply": "2024-02-27T14:20:04.094915Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.078910Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number 1\n",
      "Number 2\n",
      "Number 3\n",
      "Number 4\n"
     ]
    }
   ],
   "source": [
    "# while循环\n",
    "i = 1\n",
    "while i < 5:\n",
    "    print(\"Number\", i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.098916Z",
     "iopub.status.busy": "2024-02-27T14:20:04.098530Z",
     "iopub.status.idle": "2024-02-27T14:20:04.126300Z",
     "shell.execute_reply": "2024-02-27T14:20:04.125040Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.098884Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number 1\n",
      "Number 2\n"
     ]
    }
   ],
   "source": [
    "# break的使用（for循环同理）\n",
    "# 跳出循环\n",
    "i = 1\n",
    "while i < 5:\n",
    "    if i == 3:\n",
    "        break\n",
    "    print(\"Number\", i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.128786Z",
     "iopub.status.busy": "2024-02-27T14:20:04.128260Z",
     "iopub.status.idle": "2024-02-27T14:20:04.142850Z",
     "shell.execute_reply": "2024-02-27T14:20:04.141559Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.128746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number 1\n",
      "Number 2\n",
      "Number 4\n"
     ]
    }
   ],
   "source": [
    "# continue的使用（for循环同理）\n",
    "# 跳过本次循环\n",
    "i = 1\n",
    "while i < 5:\n",
    "    if i == 3:\n",
    "        i += 1\n",
    "        continue\n",
    "    print(\"Number\", i)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.6 List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.145438Z",
     "iopub.status.busy": "2024-02-27T14:20:04.144772Z",
     "iopub.status.idle": "2024-02-27T14:20:04.159169Z",
     "shell.execute_reply": "2024-02-27T14:20:04.158060Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.145285Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xyz\n",
      "['2', 5, 'xyz', True, 'last']\n",
      "last\n"
     ]
    }
   ],
   "source": [
    "data = [1, 5, \"xyz\", True, \"last\"]\n",
    "print(data[2])\n",
    "\n",
    "data[0] = \"2\"\n",
    "print(data)\n",
    "\n",
    "print(data[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.162155Z",
     "iopub.status.busy": "2024-02-27T14:20:04.160975Z",
     "iopub.status.idle": "2024-02-27T14:20:04.175559Z",
     "shell.execute_reply": "2024-02-27T14:20:04.174111Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.162101Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "5\n",
      "xyz\n",
      "True\n",
      "last\n",
      "yes!\n"
     ]
    }
   ],
   "source": [
    "# 遍历列表\n",
    "for i in data:\n",
    "    print(i)\n",
    "\n",
    "# 判断元素是否存在\n",
    "if \"xyz\" in data:\n",
    "    print(\"yes!\")\n",
    "else:\n",
    "    print(\"no!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.177833Z",
     "iopub.status.busy": "2024-02-27T14:20:04.177432Z",
     "iopub.status.idle": "2024-02-27T14:20:04.189919Z",
     "shell.execute_reply": "2024-02-27T14:20:04.188468Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.177799Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2', 5, 'xyz', True, 'last']\n",
      "['2', 5, 'xyz']\n",
      "[True, 'last']\n",
      "['2', 5, 'xyz', True]\n",
      "['2', 'xyz', 'last']\n",
      "['last', True, 'xyz', 5, '2']\n"
     ]
    }
   ],
   "source": [
    "# 切片 (取前不取后)\n",
    "print(data)\n",
    "# 取索引0到2的值\n",
    "print(data[0:3])\n",
    "# 取索引3到最后的值\n",
    "print(data[3:])\n",
    "# 取开头到倒数第二的值\n",
    "print(data[:-1])\n",
    "\n",
    "# 每次索引+2（默认+1），即跳过一项\n",
    "print(data[::2])\n",
    "# 每次索引-1，即倒序\n",
    "print(data[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.192823Z",
     "iopub.status.busy": "2024-02-27T14:20:04.192175Z",
     "iopub.status.idle": "2024-02-27T14:20:04.206164Z",
     "shell.execute_reply": "2024-02-27T14:20:04.204927Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.192784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "[1, 2, 3, 4]\n",
      "[1, 2, 3, 4, 5, 6]\n",
      "6 [1, 2, 3, 4, 5]\n",
      "1 [2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "data = [1, 2, 3]\n",
    "\n",
    "# 列表长度\n",
    "print(len(data))\n",
    "\n",
    "# 增加元素\n",
    "data.append(4)\n",
    "print(data)\n",
    "\n",
    "# 扩展列表\n",
    "data = data + [5, 6]\n",
    "print(data)\n",
    "\n",
    "# 从队尾取一元素，并从列表中删除\n",
    "n = data.pop()\n",
    "print(n, data)\n",
    "\n",
    "# 取索引0的元素（队头），并从列表中删除\n",
    "n = data.pop(0)\n",
    "print(n, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.7 Dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:04.208376Z",
     "iopub.status.busy": "2024-02-27T14:20:04.207864Z",
     "iopub.status.idle": "2024-02-27T14:20:04.624844Z",
     "shell.execute_reply": "2024-02-27T14:20:04.622226Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.208333Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v\n",
      "10086\n",
      "None\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "999",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[24], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28mprint\u001b[39m(data\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;241m999\u001b[39m))\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# 报错\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m999\u001b[39;49m\u001b[43m]\u001b[49m)\n",
      "\u001b[0;31mKeyError\u001b[0m: 999"
     ]
    }
   ],
   "source": [
    "data = {\"k\": \"v\", 648: 10086}\n",
    "\n",
    "print(data[\"k\"])\n",
    "print(data.get(648))\n",
    "# 返回None\n",
    "print(data.get(999))\n",
    "# 报错\n",
    "print(data[999])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:11.344874Z",
     "iopub.status.busy": "2024-02-27T14:20:11.344453Z",
     "iopub.status.idle": "2024-02-27T14:20:11.351927Z",
     "shell.execute_reply": "2024-02-27T14:20:11.350419Z",
     "shell.execute_reply.started": "2024-02-27T14:20:11.344841Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{123: 321, 456: 654, 999: 666}\n"
     ]
    }
   ],
   "source": [
    "data = {123: 321, 456: 654}\n",
    "data[999] = 666\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:12.961336Z",
     "iopub.status.busy": "2024-02-27T14:20:12.960773Z",
     "iopub.status.idle": "2024-02-27T14:20:12.970791Z",
     "shell.execute_reply": "2024-02-27T14:20:12.968979Z",
     "shell.execute_reply.started": "2024-02-27T14:20:12.961290Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123\n",
      "456\n",
      "999\n",
      "==========\n",
      "321\n",
      "654\n",
      "666\n",
      "==========\n",
      "123 321\n",
      "456 654\n",
      "999 666\n"
     ]
    }
   ],
   "source": [
    "# 各种遍历\n",
    "for k in data:\n",
    "    print(k)\n",
    "\n",
    "print(\"==========\")\n",
    "\n",
    "for v in data.values():\n",
    "    print(v)\n",
    "\n",
    "print(\"==========\")\n",
    "\n",
    "for k, v in data.items():\n",
    "    print(k, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.8 function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:16.432482Z",
     "iopub.status.busy": "2024-02-27T14:20:16.431990Z",
     "iopub.status.idle": "2024-02-27T14:20:16.440086Z",
     "shell.execute_reply": "2024-02-27T14:20:16.438600Z",
     "shell.execute_reply.started": "2024-02-27T14:20:16.432441Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "def add(a, b):\n",
    "    print(a + b)\n",
    "\n",
    "\n",
    "add(1, 4)\n",
    "add(4, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:18.127973Z",
     "iopub.status.busy": "2024-02-27T14:20:18.127518Z",
     "iopub.status.idle": "2024-02-27T14:20:18.136427Z",
     "shell.execute_reply": "2024-02-27T14:20:18.134775Z",
     "shell.execute_reply.started": "2024-02-27T14:20:18.127925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Username is admin\n",
      "Username is li\n",
      "Age is 24\n"
     ]
    }
   ],
   "source": [
    "# 默认参数\n",
    "def user_details(username, age=None):\n",
    "    print(\"Username is\", username)\n",
    "    if age:\n",
    "        print(\"Age is\", age)\n",
    "\n",
    "\n",
    "user_details(\"admin\")\n",
    "user_details(\"li\", 24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.9 more\n",
    "\n",
    "**恭喜同学们已经入门python了！！！**\n",
    "\n",
    "还有几个知识点感兴趣的同学可以自行学习：`tuple`, `set`, `class`。\n",
    "\n",
    "更多python知识可学习：https://python-cookbook.readthedocs.io/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 图搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 广度优先搜索\n",
    "下图分别为无环图与有环图，请通过编程实现广度优先搜索算法，搜索任意两点的最短路径。\n",
    "\n",
    "* 算法输入输出要求：\n",
    "  * 输入：图；起始点；终点。\n",
    "  * 输出：路径；路径长度。\n",
    "\n",
    "* 例：\n",
    "  * 无环图起始点A，终点L：['A', 'B', 'F', 'L'], 3\n",
    "  * 有环图起始点D，终点J：['D', 'A', 'B', 'E', 'J'], 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:20.362758Z",
     "iopub.status.busy": "2024-02-27T14:20:20.362379Z",
     "iopub.status.idle": "2024-02-27T14:20:20.372656Z",
     "shell.execute_reply": "2024-02-27T14:20:20.371463Z",
     "shell.execute_reply.started": "2024-02-27T14:20:20.362729Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://mermaid.ink/img/CmdyYXBoIFREOwogICAgQSAtLS0gQiAmIEMgJiBEOwogICAgQiAtLS0gRSAmIEY7CiAgICBDIC0tLSBHOwogICAgRCAtLS0gSCAmIEk7CiAgICBFIC0tLSBKOwogICAgRiAtLS0gSyAmIEw7CiAgICBHIC0tLSBNOwogICAgSSAtLS0gTjsK?bgColor=FFFFFF\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm(\n",
    "    \"\"\"\n",
    "graph TD;\n",
    "    A --- B & C & D;\n",
    "    B --- E & F;\n",
    "    C --- G;\n",
    "    D --- H & I;\n",
    "    E --- J;\n",
    "    F --- K & L;\n",
    "    G --- M;\n",
    "    I --- N;\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:22.852409Z",
     "iopub.status.busy": "2024-02-27T14:20:22.850772Z",
     "iopub.status.idle": "2024-02-27T14:20:22.862402Z",
     "shell.execute_reply": "2024-02-27T14:20:22.861111Z",
     "shell.execute_reply.started": "2024-02-27T14:20:22.852336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://mermaid.ink/img/CmdyYXBoIFREOwogICAgQSAtLS0gQiAmIEMgJiBEOwogICAgQiAtLS0gQyAmIEUgJiBGOwogICAgQyAtLS0gRzsKICAgIEQgLS0tIEggJiBJOwogICAgRSAtLS0gSjsKICAgIEYgLS0tIEogJiBLICYgTDsKICAgIEcgLS0tIEggJiBMICYgTTsKICAgIEkgLS0tIE07CiAgICBKIC0tLSBLOwo=?bgColor=FFFFFF\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm(\n",
    "    \"\"\"\n",
    "graph TD;\n",
    "    A --- B & C & D;\n",
    "    B --- C & E & F;\n",
    "    C --- G;\n",
    "    D --- H & I;\n",
    "    E --- J;\n",
    "    F --- J & K & L;\n",
    "    G --- H & L & M;\n",
    "    I --- M;\n",
    "    J --- K;\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**如何使用python表示图？**\n",
    "要求会用，不强制要求理解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:24.995256Z",
     "iopub.status.busy": "2024-02-27T14:20:24.994751Z",
     "iopub.status.idle": "2024-02-27T14:20:25.005876Z",
     "shell.execute_reply": "2024-02-27T14:20:25.004601Z",
     "shell.execute_reply.started": "2024-02-27T14:20:24.995211Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1. 邻接矩阵\n",
    "class AdjacencyMatrixGraph:\n",
    "    def __init__(self, nodes_name: List[str], adjacency_matrix: List[List[bool]]):\n",
    "        assert len(nodes_name) == len(adjacency_matrix)\n",
    "        assert len(adjacency_matrix) == len(adjacency_matrix[0])\n",
    "        self.nodes_name = nodes_name\n",
    "        self.adjacency_matrix: List[List[bool]] = adjacency_matrix\n",
    "\n",
    "    def get_neighbors(self, node):\n",
    "        idx = self.nodes_name.index(node)\n",
    "        return [\n",
    "            self.nodes_name[i] for i, n in enumerate(self.adjacency_matrix[idx]) if n\n",
    "        ]\n",
    "\n",
    "    def get_cost(self, node1: str, node2: str):\n",
    "        idx1 = self.nodes_name.index(node1)\n",
    "        idx2 = self.nodes_name.index(node2)\n",
    "        ret = self.adjacency_matrix[idx1][idx2]\n",
    "        if ret == 0:\n",
    "            return float(\"inf\")\n",
    "        else:\n",
    "            return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:26.592717Z",
     "iopub.status.busy": "2024-02-27T14:20:26.591576Z",
     "iopub.status.idle": "2024-02-27T14:20:26.613024Z",
     "shell.execute_reply": "2024-02-27T14:20:26.611577Z",
     "shell.execute_reply.started": "2024-02-27T14:20:26.592664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B', 'C', 'D']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以图1无环图为例\n",
    "adjacency_matrix_1 = [\n",
    "    [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n",
    "    [0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
    "    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0],\n",
    "    [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
    "    [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],\n",
    "    [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
    "    [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
    "]\n",
    "nodes_name_1 = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\", \"M\", \"N\"]\n",
    "graph_1 = AdjacencyMatrixGraph(nodes_name_1, adjacency_matrix_1)\n",
    "graph_1.get_neighbors(\"A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:28.214293Z",
     "iopub.status.busy": "2024-02-27T14:20:28.213597Z",
     "iopub.status.idle": "2024-02-27T14:20:28.230227Z",
     "shell.execute_reply": "2024-02-27T14:20:28.228584Z",
     "shell.execute_reply.started": "2024-02-27T14:20:28.214233Z"
    }
   },
   "outputs": [],
   "source": [
    "# 2. 邻接表\n",
    "class AdjacencyMapGraph:\n",
    "    def __init__(self, edges: List[Tuple[str, str]], weight: List[float] = None):\n",
    "        self.adjacency_map = defaultdict(list)\n",
    "        self.weight_map = {}\n",
    "        self.add_bi_edges(edges, weight)\n",
    "\n",
    "    def add_bi_edges(self, edges: List[Tuple[str, str]], weight: List[float] = None):\n",
    "        if weight is not None:\n",
    "            assert len(edges) == len(weight)\n",
    "        for i, edge in enumerate(edges):\n",
    "            node1, node2 = edge\n",
    "            if not self.adjacency_map.get(node1) or node2 not in self.adjacency_map.get(\n",
    "                node1\n",
    "            ):\n",
    "                self.adjacency_map[node1].append(node2)\n",
    "                self.adjacency_map[node2].append(node1)\n",
    "                self.weight_map[(node1, node2)] = 1 if weight is None else weight[i]\n",
    "                self.weight_map[(node2, node1)] = 1 if weight is None else weight[i]\n",
    "\n",
    "    def get_neighbors(self, node):\n",
    "        return self.adjacency_map[node]\n",
    "\n",
    "    def get_cost(self, node1: str, node2: str):\n",
    "        ret = self.weight_map.get((node1, node2))\n",
    "        if ret is None:\n",
    "            return float(\"inf\")\n",
    "        else:\n",
    "            return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:29.970410Z",
     "iopub.status.busy": "2024-02-27T14:20:29.969861Z",
     "iopub.status.idle": "2024-02-27T14:20:29.983547Z",
     "shell.execute_reply": "2024-02-27T14:20:29.982022Z",
     "shell.execute_reply.started": "2024-02-27T14:20:29.970365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B', 'C', 'D']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以图1无环图为例\n",
    "edges_1 = [\n",
    "    (\"A\", \"B\"),\n",
    "    (\"A\", \"C\"),\n",
    "    (\"A\", \"D\"),\n",
    "    (\"B\", \"E\"),\n",
    "    (\"B\", \"F\"),\n",
    "    (\"C\", \"G\"),\n",
    "    (\"D\", \"H\"),\n",
    "    (\"D\", \"I\"),\n",
    "    (\"E\", \"J\"),\n",
    "    (\"F\", \"K\"),\n",
    "    (\"F\", \"L\"),\n",
    "    (\"G\", \"M\"),\n",
    "    (\"I\", \"N\"),\n",
    "]\n",
    "graph_1 = AdjacencyMapGraph(edges_1)\n",
    "graph_1.get_neighbors(\"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**请在此完成有环图的构建**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-02-27T14:20:04.647339Z",
     "iopub.status.idle": "2024-02-27T14:20:04.648032Z",
     "shell.execute_reply": "2024-02-27T14:20:04.647816Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.647795Z"
    }
   },
   "outputs": [],
   "source": [
    "# graph_2 = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:40.986827Z",
     "iopub.status.busy": "2024-02-27T14:20:40.986370Z",
     "iopub.status.idle": "2024-02-27T14:20:41.000172Z",
     "shell.execute_reply": "2024-02-27T14:20:40.998391Z",
     "shell.execute_reply.started": "2024-02-27T14:20:40.986793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['B', 'C', 'D']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 答案\n",
    "edges_2 = [\n",
    "    (\"A\", \"B\"),\n",
    "    (\"A\", \"C\"),\n",
    "    (\"A\", \"D\"),\n",
    "    (\"B\", \"C\"),\n",
    "    (\"B\", \"E\"),\n",
    "    (\"B\", \"F\"),\n",
    "    (\"C\", \"G\"),\n",
    "    (\"D\", \"H\"),\n",
    "    (\"D\", \"I\"),\n",
    "    (\"E\", \"J\"),\n",
    "    (\"F\", \"J\"),\n",
    "    (\"F\", \"K\"),\n",
    "    (\"F\", \"L\"),\n",
    "    (\"G\", \"H\"),\n",
    "    (\"G\", \"L\"),\n",
    "    (\"G\", \"M\"),\n",
    "    (\"I\", \"M\"),\n",
    "    (\"J\", \"K\"),\n",
    "]\n",
    "costs_2 = [5, 3, 2, 8, 4, 1, 9, 7, 12, 5, 14, 3, 1, 2, 1, 1, 4, 4]\n",
    "graph_2 = AdjacencyMapGraph(edges_2, costs_2)\n",
    "graph_2.get_neighbors(\"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**请在此实现广度优先搜索算法**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-02-27T14:20:04.651305Z",
     "iopub.status.idle": "2024-02-27T14:20:04.651728Z",
     "shell.execute_reply": "2024-02-27T14:20:04.651544Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.651526Z"
    }
   },
   "outputs": [],
   "source": [
    "def bfs(graph, start: str, dest: str):\n",
    "    # 初始化一些东西\n",
    "    path = []\n",
    "\n",
    "    # 循环，从起点开始，使用graph.get_neighbors获取其邻居\n",
    "\n",
    "    # 遍历其邻居是否为终点，若不是则继续判断邻居的邻居\n",
    "\n",
    "    # 返回路径及其路径长度\n",
    "    return path, len(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-02-27T14:20:04.653040Z",
     "iopub.status.idle": "2024-02-27T14:20:04.653472Z",
     "shell.execute_reply": "2024-02-27T14:20:04.653278Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.653261Z"
    }
   },
   "outputs": [],
   "source": [
    "print(bfs(graph_1, \"A\", \"L\"))\n",
    "print(bfs(graph_2, \"D\", \"J\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:45.116612Z",
     "iopub.status.busy": "2024-02-27T14:20:45.116219Z",
     "iopub.status.idle": "2024-02-27T14:20:45.127179Z",
     "shell.execute_reply": "2024-02-27T14:20:45.126018Z",
     "shell.execute_reply.started": "2024-02-27T14:20:45.116581Z"
    }
   },
   "outputs": [],
   "source": [
    "# 答案\n",
    "def bfs(graph, start: str, dest: str):\n",
    "    queue = [start]\n",
    "    reached: dict[str, bool] = {start: True}\n",
    "    came_from: dict[str, str] = {}\n",
    "    path = []\n",
    "\n",
    "    while len(queue):\n",
    "        current = queue.pop(0)\n",
    "        print(\"search:\", current)\n",
    "        if current == dest:\n",
    "            break\n",
    "        for next in graph.get_neighbors(current):\n",
    "            if next not in reached:\n",
    "                queue.append(next)\n",
    "                reached[next] = True\n",
    "                came_from[next] = current\n",
    "\n",
    "    if not reached.get(dest):\n",
    "        return [], 0\n",
    "\n",
    "    path = [dest]\n",
    "    current = came_from.get(dest)\n",
    "    while current is not None:\n",
    "        path.append(current)\n",
    "        current = came_from.get(current)\n",
    "    return path[::-1], len(path) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:46.890453Z",
     "iopub.status.busy": "2024-02-27T14:20:46.889982Z",
     "iopub.status.idle": "2024-02-27T14:20:46.897817Z",
     "shell.execute_reply": "2024-02-27T14:20:46.896472Z",
     "shell.execute_reply.started": "2024-02-27T14:20:46.890415Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: A\n",
      "search: B\n",
      "search: C\n",
      "search: D\n",
      "search: E\n",
      "search: F\n",
      "search: G\n",
      "search: H\n",
      "search: I\n",
      "search: J\n",
      "search: K\n",
      "search: L\n",
      "(['A', 'B', 'F', 'L'], 3)\n",
      "search: D\n",
      "search: A\n",
      "search: H\n",
      "search: I\n",
      "search: B\n",
      "search: C\n",
      "search: G\n",
      "search: M\n",
      "search: E\n",
      "search: F\n",
      "search: L\n",
      "search: J\n",
      "(['D', 'A', 'B', 'E', 'J'], 4)\n"
     ]
    }
   ],
   "source": [
    "print(bfs(graph_1, \"A\", \"L\"))\n",
    "# print(bfs(graph_2,\"D\",\"J\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 深度优先搜索\n",
    "请通过编程实现深度优先搜索算法，搜索任意两点的最短路径。并思考深度优先搜索到的路径能够保证代价最优吗？广度优先搜索呢？\n",
    "\n",
    "* 算法输入输出要求：\n",
    "  * 输入：图；起始点；终点。\n",
    "  * 输出：路径；路径长度。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-02-27T14:20:04.660494Z",
     "iopub.status.idle": "2024-02-27T14:20:04.661137Z",
     "shell.execute_reply": "2024-02-27T14:20:04.660820Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.660795Z"
    }
   },
   "outputs": [],
   "source": [
    "def dfs(graph, start: str, dest: str):\n",
    "    # 初始化一些东西\n",
    "    path = []\n",
    "\n",
    "    # 循环，从起点开始，使用graph.get_neighbors获取其邻居\n",
    "\n",
    "    # 遍历其邻居是否为终点，若不是则继续判断邻居的邻居\n",
    "    # （dfs和bfs在选取邻居的策略不一致）\n",
    "\n",
    "    # 返回路径及其路径长度\n",
    "    return path, len(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2024-02-27T14:20:04.663417Z",
     "iopub.status.idle": "2024-02-27T14:20:04.664306Z",
     "shell.execute_reply": "2024-02-27T14:20:04.663956Z",
     "shell.execute_reply.started": "2024-02-27T14:20:04.663911Z"
    }
   },
   "outputs": [],
   "source": [
    "print(dfs(graph_1, \"A\", \"L\"))\n",
    "# print(dfs(graph_2,\"D\",\"J\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:20:50.902717Z",
     "iopub.status.busy": "2024-02-27T14:20:50.902270Z",
     "iopub.status.idle": "2024-02-27T14:20:50.918612Z",
     "shell.execute_reply": "2024-02-27T14:20:50.917146Z",
     "shell.execute_reply.started": "2024-02-27T14:20:50.902684Z"
    }
   },
   "outputs": [],
   "source": [
    "# 答案\n",
    "def dfs(graph, start: str, dest: str):\n",
    "    queue = [start]\n",
    "    reached: dict[str, bool] = {start: True}\n",
    "    came_from: dict[str, str] = {}\n",
    "    path = []\n",
    "\n",
    "    while len(queue):\n",
    "        current = queue.pop()\n",
    "        print(\"search:\", current)\n",
    "        if current == dest:\n",
    "            break\n",
    "        for next in graph.get_neighbors(current):\n",
    "            if next not in reached:\n",
    "                queue.append(next)\n",
    "                reached[next] = True\n",
    "                came_from[next] = current\n",
    "\n",
    "    if not reached.get(dest):\n",
    "        return [], 0\n",
    "\n",
    "    path = [dest]\n",
    "    current = came_from.get(dest)\n",
    "    while current is not None:\n",
    "        path.append(current)\n",
    "        current = came_from.get(current)\n",
    "    return path[::-1], len(path) - 1\n",
    "\n",
    "\n",
    "def dfs_recursion(graph, start: str, dest: str):\n",
    "    reached: dict[str, bool] = {start: True}\n",
    "\n",
    "    def _dfs_recursion(graph, start: str, dest: str):\n",
    "        print(\"search:\", start)\n",
    "        neighbors = [n for n in graph.get_neighbors(start) if n not in reached]\n",
    "        if dest in neighbors:\n",
    "            return [dest, start], 1\n",
    "        else:\n",
    "            for current in neighbors:\n",
    "                reached[current] = True\n",
    "                path, cost = _dfs_recursion(graph, current, dest)\n",
    "                if cost:\n",
    "                    path.append(start)\n",
    "                    return path, cost + 1\n",
    "            return [], 0\n",
    "\n",
    "    path, cost = _dfs_recursion(graph, start, dest)\n",
    "    return path[::-1], cost"
   ]
  },
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   "cell_type": "code",
   "execution_count": 39,
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: A\n",
      "search: D\n",
      "search: I\n",
      "search: N\n",
      "search: H\n",
      "search: C\n",
      "search: G\n",
      "search: M\n",
      "search: B\n",
      "search: F\n",
      "search: L\n",
      "(['A', 'B', 'F', 'L'], 3)\n",
      "==========\n",
      "search: A\n",
      "search: B\n",
      "search: E\n",
      "search: J\n",
      "search: F\n",
      "(['A', 'B', 'F', 'L'], 3)\n",
      "==========\n",
      "search: D\n",
      "search: I\n",
      "search: M\n",
      "search: G\n",
      "search: L\n",
      "search: F\n",
      "search: K\n",
      "search: J\n",
      "(['D', 'I', 'M', 'G', 'L', 'F', 'J'], 6)\n",
      "==========\n",
      "search: D\n",
      "search: A\n",
      "search: B\n",
      "search: C\n",
      "search: G\n",
      "search: H\n",
      "search: L\n",
      "search: F\n",
      "(['D', 'A', 'B', 'C', 'G', 'L', 'F', 'J'], 7)\n"
     ]
    }
   ],
   "source": [
    "print(dfs(graph_1, \"A\", \"L\"))\n",
    "print(\"==========\")\n",
    "print(dfs_recursion(graph_1, \"A\", \"L\"))\n",
    "print(\"==========\")\n",
    "print(dfs(graph_2, \"D\", \"J\"))\n",
    "print(\"==========\")\n",
    "print(dfs_recursion(graph_2, \"D\", \"J\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 有权图搜索\n",
    "下图是有权图，其权重表示这两个节点之间的代价。自行学习AdjacencyMatrixGraph或AdjacencyMapGraph代码，使用广度优先搜索算法与深度优先搜索算法对下面有权图搜索任意两点的路径，并给出该路径的总代价，并思考广度优先搜索算法或深度优先搜索算法搜索在有权图搜索中的路径是代价最优的吗？\n",
    "\n",
    "* 算法输入输出要求：\n",
    "  * 输入：图；起始点；终点。\n",
    "  * 输出：路径；路径长度。"
   ]
  },
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    {
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    "mm(\n",
    "    \"\"\"\n",
    "graph TD\n",
    "    A -- 5 --- B;\n",
    "    A -- 3 --- C;\n",
    "    A -- 2 --- D;\n",
    "    B -- 8 --- C;\n",
    "    B -- 4 --- E;\n",
    "    B -- 1 --- F;\n",
    "    C -- 9 --- G;\n",
    "    D -- 7 --- H;\n",
    "    D -- 12 --- I;\n",
    "    E -- 5 --- J;\n",
    "    F -- 14 --- J;\n",
    "    F -- 3 --- K;\n",
    "    F -- 1 --- L;\n",
    "    G -- 2 --- H;\n",
    "    G -- 1 --- L;\n",
    "    G -- 1 --- M;\n",
    "    I -- 4 --- M;\n",
    "    J -- 4 --- K;\n",
    "\"\"\"\n",
    ")"
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   "source": [
    "# 答案\n",
    "def bfs_with_weight(graph, start: str, dest: str):\n",
    "    queue = [start]\n",
    "    reached: dict[str, bool] = {start: True}\n",
    "    came_from: dict[str, str] = {}\n",
    "    path = []\n",
    "    cost = 0\n",
    "\n",
    "    while len(queue):\n",
    "        current = queue.pop(0)\n",
    "        print(\"search:\", current)\n",
    "        if current == dest:\n",
    "            break\n",
    "        for next in graph.get_neighbors(current):\n",
    "            if next not in reached:\n",
    "                queue.append(next)\n",
    "                reached[next] = True\n",
    "                came_from[next] = current\n",
    "\n",
    "    if not reached.get(dest):\n",
    "        return [], 0\n",
    "\n",
    "    path = [dest]\n",
    "    current = came_from.get(dest)\n",
    "    while current is not None:\n",
    "        cost += graph.get_cost(current, path[-1])\n",
    "        path.append(current)\n",
    "        current = came_from.get(current)\n",
    "    return path[::-1], cost\n",
    "\n",
    "\n",
    "def dfs_with_weight(graph, start: str, dest: str):\n",
    "    queue = [start]\n",
    "    reached: dict[str, bool] = {start: True}\n",
    "    came_from: dict[str, str] = {}\n",
    "    path = []\n",
    "    cost = 0\n",
    "\n",
    "    while len(queue):\n",
    "        current = queue.pop()\n",
    "        print(\"search:\", current)\n",
    "        if current == dest:\n",
    "            break\n",
    "        for next in graph.get_neighbors(current):\n",
    "            if next not in reached:\n",
    "                queue.append(next)\n",
    "                reached[next] = True\n",
    "                came_from[next] = current\n",
    "\n",
    "    if not reached.get(dest):\n",
    "        return [], 0\n",
    "\n",
    "    path = [dest]\n",
    "    current = came_from.get(dest)\n",
    "    while current is not None:\n",
    "        cost += graph.get_cost(current, path[-1])\n",
    "        path.append(current)\n",
    "        current = came_from.get(current)\n",
    "    return path[::-1], cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-02-27T14:21:09.479133Z",
     "iopub.status.busy": "2024-02-27T14:21:09.478719Z",
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     "shell.execute_reply": "2024-02-27T14:21:09.484766Z",
     "shell.execute_reply.started": "2024-02-27T14:21:09.479099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: D\n",
      "search: A\n",
      "search: H\n",
      "search: I\n",
      "search: B\n",
      "search: C\n",
      "search: G\n",
      "search: M\n",
      "search: E\n",
      "search: F\n",
      "search: L\n",
      "search: J\n",
      "(['D', 'A', 'B', 'E', 'J'], 16)\n",
      "==========\n",
      "search: D\n",
      "search: I\n",
      "search: M\n",
      "search: G\n",
      "search: L\n",
      "search: F\n",
      "search: K\n",
      "search: J\n",
      "(['D', 'I', 'M', 'G', 'L', 'F', 'J'], 33)\n"
     ]
    }
   ],
   "source": [
    "print(bfs_with_weight(graph_2, \"D\", \"J\"))\n",
    "print(\"==========\")\n",
    "print(dfs_with_weight(graph_2, \"D\", \"J\"))"
   ]
  }
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